def tsunami_waveforms(home,project_name,fault_name,rupture_name,station_file,model_name,run_name,GF_list,G_from_file,G_name,epicenter,
rupture_speed,num_windows,coord_type,decimate,lowpass,resample,beta):
'''
Forward compute tsunami waveforms the right way, load the GF matrix and just multiply by fault model
'''
from mudpy.inverse import getG
from numpy import genfromtxt,zeros,arange,deg2rad,cos,sin,vstack,array,where
from obspy import read
#Read GFs
G=getG(home,project_name,fault_name,model_name,GF_list,G_from_file,G_name,epicenter,
rupture_speed,num_windows,coord_type,decimate,lowpass)
#Read rupture model and convert into vector
ss=genfromtxt(home+project_name+'/forward_models/'+rupture_name,usecols=8)
ds=genfromtxt(home+project_name+'/forward_models/'+rupture_name,usecols=9)
#Rotate by beta
if beta != None:
beta_rot=deg2rad(beta)
R=array([[cos(beta_rot),sin(beta_rot)],[-sin(beta_rot),cos(beta_rot)]])
rot=R.dot(vstack((ss,ds)))
ss_rot=rot[0,:]
ds_rot=rot[1,:]
#Assemble into column vector
m=zeros(len(ss)*2)
iss=arange(0,len(m),2)
ids=arange(1,len(m),2)
m[iss]=ss_rot
m[ids]=ds_rot
#Multiply
dtsun=G.dot(m)
#Write to file (Modified from inverse.write_synthetics)
#Read gf file and decide what needs to get loaded
gf_file=home+project_name+'/data/station_info/'+GF_list
stations=genfromtxt(gf_file,usecols=[0],skip_header=1,dtype='S')
GF=genfromtxt(gf_file,usecols=[3,4,5,6,7],skip_header=1,dtype='f8')
GFfiles=genfromtxt(gf_file,usecols=[8,9,10,11,12],skip_header=1,dtype='S')
#Separate into its constituent parts (statics,displacaments, velocities, etc...)
kinsert=0
kgf=3
i=where(GF[:,kgf]==1)[0]
if len(i)>0:
for ksta in range(len(i)):
sta=stations[i[ksta]]
tsun=read(GFfiles[i[ksta],kgf])
npts=tsun[0].stats.npts
synth=tsun.copy()
synth[0].data=dtsun[kinsert:kinsert+npts]
kinsert+=npts
synth.write(home+project_name+'/output/forward_models/'+run_name+'.'+sta+'.tsun',format='SAC')
def run_inversion(home,
project_name,
run_name,
fault_name,
model_name,
GF_list,
G_from_file,
G_name,
epicenter,
rupture_speed,
num_windows,
reg_spatial,
reg_temporal,
nfaults,
beta,
decimate,
bandpass,
solver,
bounds,
weight=False,
Ltype=2,
target_moment=None,
data_vector=None):
'''
Assemble G and d, determine smoothing and run the inversion
'''
from mudpy import inverse as inv
from mudpy.forward import get_mu_and_area
from numpy import zeros, dot, array, squeeze, expand_dims, empty, tile, eye, ones, arange, load, size
from numpy.linalg import lstsq
from scipy.sparse import csr_matrix as sparse
from scipy.optimize import nnls
from datetime import datetime
import gc
t1 = datetime.now()
#Get data vector
if data_vector == None:
d = inv.getdata(home,
project_name,
GF_list,
decimate,
bandpass=bandpass)
else:
d = load(data_vector)
#Get GFs
G = inv.getG(home, project_name, fault_name, model_name, GF_list,
G_from_file, G_name, epicenter, rupture_speed, num_windows,
decimate, bandpass)
gc.collect()
#Get data weights
if weight == True:
print 'Applying data weights'
w = inv.get_data_weights(home, project_name, GF_list, d, decimate)
W = empty(G.shape)
W = tile(w, (G.shape[1], 1)).T
WG = empty(G.shape)
WG = W * G
wd = w * d.squeeze()
wd = expand_dims(wd, axis=1)
#Clear up extraneous variables
W = None
w = None
#Define inversion quantities
x = WG.transpose().dot(wd)
print 'Computing G\'G'
K = (WG.T).dot(WG)
else:
#Define inversion quantities if no weightd
x = G.transpose().dot(d)
print 'Computing G\'G'
K = (G.T).dot(G)
#Get regularization matrices (set to 0 matrix if not needed)
static = False #Is it jsut a static inversion?
if size(reg_spatial) > 1:
if Ltype == 2: #Laplacian smoothing
Ls = inv.getLs(home, project_name, fault_name, nfaults,
num_windows, bounds)
elif Ltype == 0: #Tikhonov smoothing
N = nfaults[0] * nfaults[
1] * num_windows * 2 #Get total no. of model parameters
Ls = eye(N)
elif Ltype == 3: #moment regularization
N = nfaults[0] * nfaults[
1] * num_windows * 2 #Get total no. of model parameters
Ls = ones((1, N))
#Add rigidity and subfault area
mu, area = get_mu_and_area(home, project_name, fault_name,
model_name)
istrike = arange(0, N, 2)
Ls[0, istrike] = area * mu
idip = arange(1, N, 2)
Ls[0, idip] = area * mu
#Modify inversion quantities
x = x + Ls.T.dot(target_moment)
else:
print 'ERROR: Unrecognized regularization type requested'
return
Ninversion = len(reg_spatial)
else:
Ls = zeros(K.shape)
reg_spatial = array([0.])
Ninversion = 1
if size(reg_temporal) > 1:
Lt = inv.getLt(home, project_name, fault_name, num_windows)
Ninversion = len(reg_temporal) * Ninversion
else:
Lt = zeros(K.shape)
reg_temporal = array([0.])
static = True
#Make L's sparse
Ls = sparse(Ls)
Lt = sparse(Lt)
#Get regularization tranposes for ABIC
LsLs = Ls.transpose().dot(Ls)
LtLt = Lt.transpose().dot(Lt)
#inflate
Ls = Ls.todense()
Lt = Lt.todense()
LsLs = LsLs.todense()
LtLt = LtLt.todense()
#off we go
dt = datetime.now() - t1
print 'Preprocessing wall time was ' + str(dt)
print '\n--- RUNNING INVERSIONS ---\n'
ttotal = datetime.now()
kout = 0
for kt in range(len(reg_temporal)):
for ks in range(len(reg_spatial)):
t1 = datetime.now()
lambda_spatial = reg_spatial[ks]
lambda_temporal = reg_temporal[kt]
print 'Running inversion ' + str(kout + 1) + ' of ' + str(
Ninversion) + ' at regularization levels: ls =' + repr(
lambda_spatial) + ' , lt = ' + repr(lambda_temporal)
if static == True: #Only statics inversion no Lt matrix
Kinv = K + (lambda_spatial**2) * LsLs
Lt = eye(len(K))
LtLt = Lt.T.dot(Lt)
else: #Mixed inversion
Kinv = K + (lambda_spatial**2) * LsLs + (lambda_temporal**
2) * LtLt
if solver.lower() == 'lstsq':
sol, res, rank, s = lstsq(Kinv, x)
elif solver.lower() == 'nnls':
x = squeeze(x.T)
try:
sol, res = nnls(Kinv, x)
except:
print '+++ WARNING: No solution found, writting zeros.'
sol = zeros(G.shape[1])
x = expand_dims(x, axis=1)
sol = expand_dims(sol, axis=1)
else:
print 'ERROR: Unrecognized solver \'' + solver + '\''
#Compute synthetics
ds = dot(G, sol)
#Get stats
L2, Lmodel = inv.get_stats(Kinv, sol, x)
VR = inv.get_VR(home, project_name, GF_list, sol, d, ds, decimate)
#VR=inv.get_VR(WG,sol,wd)
#ABIC=inv.get_ABIC(WG,K,sol,wd,lambda_spatial,lambda_temporal,Ls,LsLs,Lt,LtLt)
ABIC = inv.get_ABIC(G, K, sol, d, lambda_spatial, lambda_temporal,
Ls, LsLs, Lt, LtLt)
#Get moment
Mo, Mw = inv.get_moment(home, project_name, fault_name, model_name,
sol)
#If a rotational offset was applied then reverse it for output to file
if beta != 0:
sol = inv.rot2ds(sol, beta)
#Write log
inv.write_log(home, project_name, run_name, kout, rupture_speed,
num_windows, lambda_spatial, lambda_temporal, beta,
L2, Lmodel, VR, ABIC, Mo, Mw, model_name, fault_name,
G_name, GF_list, solver)
#Write output to file
inv.write_synthetics(home, project_name, run_name, GF_list, G, sol,
ds, kout, decimate)
inv.write_model(home, project_name, run_name, fault_name,
model_name, rupture_speed, num_windows, epicenter,
sol, kout)
kout += 1
dt1 = datetime.now() - t1
dt2 = datetime.now() - ttotal
print '... inversion wall time was ' + str(
dt1) + ', total wall time elapsed is ' + str(dt2)
def run_inversion(home,
project_name,
run_name,
fault_name,
model_name,
GF_list,
G_from_file,
G_name,
epicenter,
rupture_speed,
num_windows,
reg_spatial,
reg_temporal,
nfaults,
beta,
decimate,
bandpass,
solver,
bounds,
weight=False,
Ltype=2,
target_moment=None,
data_vector=None,
weights_file=None,
onset_file=None,
GOD_inversion=False):
'''
Assemble G and d, determine smoothing and run the inversion
'''
from mudpy import inverse as inv
from mudpy.forward import get_mu_and_area
from numpy import zeros, dot, array, squeeze, expand_dims, empty, tile, eye, ones, arange, load, size, genfromtxt
from numpy import where, sort, r_, diag
from numpy.linalg import lstsq
from scipy.linalg import norm
from scipy.sparse import csr_matrix as sparse
from scipy.optimize import nnls
from datetime import datetime
import gc
from matplotlib import path
t1 = datetime.now()
#Get data vector
if data_vector == None:
d = inv.getdata(home,
project_name,
GF_list,
decimate,
bandpass=bandpass)
else:
d = load(data_vector)
#Get GFs
G = inv.getG(home,
project_name,
fault_name,
model_name,
GF_list,
G_from_file,
G_name,
epicenter,
rupture_speed,
num_windows,
decimate,
bandpass,
onset_file=onset_file)
print(G.shape)
gc.collect()
#Get data weights
if weight == True:
print('Applying data weights')
if weights_file == None:
w = inv.get_data_weights(home, project_name, GF_list, d, decimate)
else: # Remember weights are "uncertainties" alrger value is less trustworthy
w = genfromtxt(weights_file)
w = 1 / w
#apply data weights
wd = w * d.squeeze()
#get norm after applying weights and normalize weghted data vector
data_norm = norm(wd)
wd = wd / data_norm
#reshape for inversion
wd = expand_dims(wd, axis=1)
#Apply weights to left hand side of the equation (the GFs)
W = empty(G.shape)
#W=tile(w,(G.shape[1],1)).T #why this and not a diagonal matrix??? ANSWER: they have the same effect, don;t rememebr why I chose to do it this way
W = diag(w)
#Normalization effect
W = W / data_norm
WG = W.dot(G)
#Clear up extraneous variables
W = None
w = None
#Define inversion quantities
x = WG.transpose().dot(wd)
print('Computing G\'G')
K = (WG.T).dot(WG)
else:
#Define inversion quantities if no weighted
x = G.transpose().dot(d)
print('Computing G\'G')
K = (G.T).dot(G)
#Get regularization matrices (set to 0 matrix if not needed)
static = False #Is it jsut a static inversion?
if size(reg_spatial) > 1:
if Ltype == 2: #Laplacian smoothing
Ls = inv.getLs(home, project_name, fault_name, nfaults,
num_windows, bounds)
elif Ltype == 0: #Tikhonov smoothing
N = nfaults[0] * nfaults[
1] * num_windows * 2 #Get total no. of model parameters
Ls = eye(N)
elif Ltype == 3: #moment regularization
N = nfaults[0] * nfaults[
1] * num_windows * 2 #Get total no. of model parameters
Ls = ones((1, N))
#Add rigidity and subfault area
mu, area = get_mu_and_area(home, project_name, fault_name,
model_name)
istrike = arange(0, N, 2)
Ls[0, istrike] = area * mu
idip = arange(1, N, 2)
Ls[0, idip] = area * mu
#Modify inversion quantities
x = x + Ls.T.dot(target_moment)
else:
print('ERROR: Unrecognized regularization type requested')
return
Ninversion = len(reg_spatial)
else:
Ls = zeros(K.shape)
reg_spatial = array([0.])
Ninversion = 1
if size(reg_temporal) > 1:
Lt = inv.getLt(home, project_name, fault_name, num_windows)
Ninversion = len(reg_temporal) * Ninversion
else:
Lt = zeros(K.shape)
reg_temporal = array([0.])
static = True
#Make L's sparse
Ls = sparse(Ls)
Lt = sparse(Lt)
#Get regularization tranposes for ABIC
LsLs = Ls.transpose().dot(Ls)
LtLt = Lt.transpose().dot(Lt)
#inflate
Ls = Ls.todense()
Lt = Lt.todense()
LsLs = LsLs.todense()
LtLt = LtLt.todense()
#off we go
dt = datetime.now() - t1
print('Preprocessing wall time was ' + str(dt))
print('\n--- RUNNING INVERSIONS ---\n')
ttotal = datetime.now()
kout = 0
for kt in range(len(reg_temporal)):
for ks in range(len(reg_spatial)):
t1 = datetime.now()
lambda_spatial = reg_spatial[ks]
lambda_temporal = reg_temporal[kt]
print('Running inversion ' + str(kout + 1) + ' of ' +
str(Ninversion) + ' at regularization levels: ls =' +
repr(lambda_spatial) + ' , lt = ' + repr(lambda_temporal))
if static == True: #Only statics inversion no Lt matrix
Kinv = K + (lambda_spatial**2) * LsLs
Lt = eye(len(K))
LtLt = Lt.T.dot(Lt)
else: #Mixed inversion
Kinv = K + (lambda_spatial**2) * LsLs + (lambda_temporal**
2) * LtLt
if solver.lower() == 'lstsq':
sol, res, rank, s = lstsq(Kinv, x)
elif solver.lower() == 'nnls':
x = squeeze(x.T)
try:
sol, res = nnls(Kinv, x)
except:
print('+++ WARNING: No solution found, writting zeros.')
sol = zeros(G.shape[1])
x = expand_dims(x, axis=1)
sol = expand_dims(sol, axis=1)
else:
print('ERROR: Unrecognized solver \'' + solver + '\'')
#Force faults outside a polygon to be zero
# print('WARNING: Using fault polygon to force solutions to zero')
# #load faulkt
# fault=genfromtxt(home+project_name+'/data/model_info/'+fault_name)
# polygon=genfromtxt('/Users/dmelgarm/Oaxaca2020/etc/zero_fault.txt')
# polygon=path.Path(polygon)
# i=where(polygon.contains_points(fault[:,1:3])==False)[0]
# i=sort(r_[i*2,i*2+1])
# N=nfaults[0]*2
# i=r_[i,i+N,i+2*N,i+3*N]
# sol[i]=0
#Compute synthetics
ds = dot(G, sol)
#Get stats
L2, Lmodel = inv.get_stats(Kinv, sol, x, Ls)
VR, L2data = inv.get_VR(home, project_name, GF_list, sol, d, ds,
decimate, WG, wd)
#VR=inv.get_VR(WG,sol,wd)
#ABIC=inv.get_ABIC(WG,K,sol,wd,lambda_spatial,lambda_temporal,Ls,LsLs,Lt,LtLt)
ABIC = inv.get_ABIC(G, K, sol, d, lambda_spatial, lambda_temporal,
Ls, LsLs, Lt, LtLt)
#Get moment
Mo, Mw = inv.get_moment(home, project_name, fault_name, model_name,
sol)
#If a rotational offset was applied then reverse it for output to file
if beta != 0:
sol = inv.rot2ds(sol, beta)
#Write log
inv.write_log(home, project_name, run_name, kout, rupture_speed,
num_windows, lambda_spatial, lambda_temporal, beta,
L2, Lmodel, VR, ABIC, Mo, Mw, model_name, fault_name,
G_name, GF_list, solver, L2data)
#Write output to file
if GOD_inversion == True:
num = str(kout).rjust(4, '0')
np.save(
home + project_name + '/output/inverse_models/' +
run_name + '.' + num + '.syn.npy', ds)
inv.write_synthetics_GOD(home, project_name, run_name, GF_list,
ds, kout, decimate)
else:
inv.write_synthetics(home, project_name, run_name, GF_list, G,
sol, ds, kout, decimate)
inv.write_model(home,
project_name,
run_name,
fault_name,
model_name,
rupture_speed,
num_windows,
epicenter,
sol,
kout,
onset_file=onset_file)
kout += 1
dt1 = datetime.now() - t1
dt2 = datetime.now() - ttotal
print('... inversion wall time was ' + str(dt1) +
', total wall time elapsed is ' + str(dt2))
def waveforms_matrix(home,project_name,fault_name,rupture_name,station_file,GF_list,
model_name,run_name,epicenter,time_epi,integrate,tsunami,hot_start,
resample,beta,rupture_speed,num_windows,dt,NFFT):
'''
To supplant waveforms() it needs to include resmapling and all that jazz...
This routine will take synthetics and apply a slip dsitribution. It will delay each
subfault by the appropriate rupture time and linearly superimpose all of them. Output
will be one sac waveform file per direction of motion (NEU) for each station defined in the
station_file. Depending on the specified rake angle at each subfault the code will compute
the contribution to dip and strike slip directions. It will also compute the moment at that
subfault and scale it according to the unit amount of momeent (1e15 N-m)
IN:
home: Home directory
project_name: Name of the problem
rupture_name: Name of rupture description file
station_file: File with coordinates of stations
model_Name: Name of Earth structure model file
integrate: =0 if you want output to be velocity, =1 if you want output to de displacement
OUT:
Nothing
'''
from numpy import loadtxt,genfromtxt,allclose,vstack,deg2rad,array,sin,cos,where,zeros,arange
from obspy import read,Stream,Trace
from string import rjust
import datetime
import gc
from mudpy.inverse import getG
from linecache import getline
from os import remove
print 'Solving for kinematic problem'
#Output where?
outpath=home+project_name+'/output/forward_models/'
logpath=home+project_name+'/logs/'
log=''
#Time for log file
now=datetime.datetime.now()
now=now.strftime('%b-%d-%H%M')
#load source
#source=loadtxt(home+project_name+'/forward_models/'+rupture_name,ndmin=2)
#Load stations
station_file=home+project_name+'/data/station_info/'+station_file
staname=genfromtxt(station_file,dtype="S6",usecols=0)
#Now load rupture model
mss=genfromtxt(home+project_name+'/forward_models/'+rupture_name,usecols=8)
mds=genfromtxt(home+project_name+'/forward_models/'+rupture_name,usecols=9)
m=zeros(2*len(mss))
i=arange(0,2*len(mss),2)
m[i]=mss
i=arange(1,2*len(mds),2)
m[i]=mds
#What am I processing v or d ?
if integrate==1:
vord='disp'
else:
vord='vel'
#Load gflist
gfsta=genfromtxt(home+project_name+'/data/station_info/'+GF_list,usecols=0,skip_header=1,dtype='S6')
#Loop over stations
for ksta in range(hot_start,len(staname)):
print 'Working on station '+staname[ksta]+' ('+str(ksta+1)+'/'+str(len(staname))+')'
#Initalize output
n=Stream()
e=Stream()
z=Stream()
sta=staname[ksta]
#Make dummy data
ndummy=Stream(Trace())
ndummy[0].data=zeros(int(NFFT))
ndummy[0].stats.delta=dt
ndummy[0].stats.starttime=time_epi
edummy=ndummy.copy()
udummy=ndummy.copy()
ndummy.write(home+project_name+'/data/waveforms/'+sta+'.'+vord+'.n',format='SAC')
edummy.write(home+project_name+'/data/waveforms/'+sta+'.'+vord+'.e',format='SAC')
udummy.write(home+project_name+'/data/waveforms/'+sta+'.'+vord+'.u',format='SAC')
#Extract only one station from GF_list file
ista=int(where(gfsta==staname[ksta])[0])+2
#Make mini GF_file
tmpgf='tmpfwd.gflist'
try:
remove(home+project_name+'/data/station_info/'+tmpgf)
except:
pass
gflist=getline(home+project_name+'/data/station_info/'+GF_list,ista)
f=open(home+project_name+'/data/station_info/'+tmpgf,'w')
f.write('# Headers\n')
f.write(gflist)
f.close()
#Get matrix for one station to all sources
G_from_file=False
G_name='tmpfwd'
G=getG(home,project_name,fault_name,model_name,tmpgf,G_from_file,G_name,epicenter,
rupture_speed,num_windows,decimate=None,bandpass=None,tsunami=False)
# Matrix multiply and separate data streams
d=G.dot(m)
n=ndummy.copy()
e=edummy.copy()
u=udummy.copy()
ncut=len(d)/3
n[0].data=d[0:ncut]
e[0].data=d[ncut:2*ncut]
u[0].data=d[2*ncut:3*ncut]
# Write to file
n.write(home+project_name+'/output/forward_models/'+run_name+'.'+gfsta[ksta]+'.'+vord+'.n',format='SAC')
e.write(home+project_name+'/output/forward_models/'+run_name+'.'+gfsta[ksta]+'.'+vord+'.e',format='SAC')
u.write(home+project_name+'/output/forward_models/'+run_name+'.'+gfsta[ksta]+'.'+vord+'.u',format='SAC')
right = 'locked' #'locked' or 'free'
bounds = (top, bottom, left, right)
################################################################################e=
######## Run-time modifications to the time series ############
weight = True
decimate = None #Decimate by constant (=None for NO decimation)
displacement_bandpass = np.array([0.5])
velocity_bandpass = np.array([1. / 50, 0.3])
tsunami_bandpass = None #np.array([0.0001,np.inf])
bandpass = [displacement_bandpass, velocity_bandpass, tsunami_bandpass]
################################################################################
dcomplete = inv.getdata(home, project_name, GF_list, decimate, bandpass=None)
#Get GFs
Gcomplete = inv.getG(home, project_name, fault_name, model_name, GF_list, True,
G_name, epicenter, rupture_speed, num_windows, decimate,
bandpass)
print 'Applying data weights'
w = inv.get_data_weights(home, project_name, GF_list, dcomplete, decimate)
W = empty(Gcomplete.shape)
W = tile(w, (Gcomplete.shape[1], 1)).T
WG = empty(Gcomplete.shape)
WG = W * Gcomplete
wd = w * dcomplete.squeeze()
wd = expand_dims(wd, axis=1)
WGcomplete = WG.copy()
wdcomplete = wd.copy()
#Clear up extraneous variables
W = None
w = None
beta = 45 #Rotational offset (in degrees) applied to rake (0 for normal)
Ltype = 0 # 0 for Tikhonov and 2 for Laplacian
solver = 'nnls' # 'lstsq','nnls'
top = 'free'
bottom = 'locked'
left = 'locked'
right = 'locked' #'locked' or 'free'
bounds = (top, bottom, left, right)
################################################################################e=
######## Run-time modifications to the time series ############
weight = False
decimate = None #Decimate by constant (=None for NO decimation)
bandpass = None #Corner frequencies in Hz =None if no filter is desired
################################################################################
G_name = 'test'
m = zeros((600, 1))
iss = arange(0, len(m), 2)
ids = arange(1, len(m), 2)
mod = genfromtxt(
u'/Users/dmelgar/Slip_inv/Nepal_Avouac_1s/output/inverse_models/models/ALOS_GPS_3.3_20win_pulse_vall3.0001.inv.total'
)
m[iss, 0] = mod[:, 8]
m[ids, 0] = mod[:, 9]
mrot = inverse.ds2rot(m, 45)
G = inverse.getG(home, project_name, fault_name, model_name, GF_list,
G_from_file, G_name, epicenter, rupture_speed, num_windows,
decimate, bandpass)
d = inverse.getdata(home, project_name, GF_list, decimate, bandpass=None)
ds = G.dot(mrot)
def run_inversion(home,project_name,run_name,fault_name,model_name,GF_list,G_from_file,G_name,epicenter,
rupture_speed,num_windows,coord_type,reg_spatial,reg_temporal,nfaults,beta,decimate,lowpass,
solver,bounds,segs):
'''
Assemble G and d, determine smoothing and run the inversion
'''
from mudpy import inverse as inv
from mudpy import degadds
from numpy import zeros,dot,array,squeeze,expand_dims,empty,tile,floor
from numpy.linalg import lstsq
from scipy.sparse import csr_matrix as sparse
from scipy.optimize import nnls
from datetime import datetime
import gc
t1=datetime.now()
#Get data vector
d=inv.getdata(home,project_name,GF_list,decimate,lowpass)
#Get GFs
G=inv.getG(home,project_name,fault_name,model_name,GF_list,G_from_file,G_name,epicenter,
rupture_speed,num_windows,coord_type,decimate,lowpass)
gc.collect()
#Get data weights
#w=inv.get_data_weights(home,project_name,GF_list,d,decimate)
#W=empty(G.shape)
#W=tile(w,(G.shape[1],1)).T
#WG=empty(G.shape)
#WG=W*G
#wd=w*d.squeeze()
#wd=expand_dims(wd,axis=1)
##Clear up extraneous variables
#W=None
#w=None
##Define inversion quantities
#x=WG.transpose().dot(wd)
#print 'Computing G\'G'
#K=(WG.T).dot(WG)
#Define inversion quantities
x=G.transpose().dot(d)
print 'Computing G\'G'
K=(G.T).dot(G)
#Get regularization matrices (set to 0 matrix if not needed)
if type(reg_spatial)!=bool:
## DEG ADDED THIS IN TO DEAL WITH MULTIPLE FAULT SEGMENTS
#if segs==0:
# Ls=inv.getLs(home,project_name,fault_name,nfaults,num_windows,bounds,segs)
##LsLs=Ls.transpose().dot(Ls)
#elif segs==1: #This will make Ls a list of matrices, one index for each fault segment
# Ls=degadds.getLs_segs(home,project_name,fault_name,nfaults,num_windows,bounds,segs)
Ls=degadds.getLs_segs(home,project_name,fault_name,nfaults,num_windows,bounds,segs)
# LsLs=[]
# nstrike_segs=nfaults[0]
# for i in range(len(nstrike_segs)):
# LsLs0 = Ls[i].transpose().dot(Ls[i])
# LsLs.append(LsLs0)
Ninversion=len(reg_spatial)
else:
Ls=zeros(K.shape)
reg_spatial=array([0.])
Ninversion=1
if type(reg_temporal)!=bool:
Lt=inv.getLt(home,project_name,fault_name,num_windows)
Ninversion=len(reg_temporal)*Ninversion
else:
Lt=zeros(K.shape)
reg_temporal=array([0.])
#Make L's sparse <-- does this actually change anything??--DEG
#for i in range(len(nstrike_segs)):
# Ls=sparse(Ls[:,:,i])
Ls=sparse(Ls)
Lt=sparse(Lt)
#Get regularization tranposes for ABIC
LsLs=Ls.transpose().dot(Ls)
LtLt=Lt.transpose().dot(Lt)
#inflate
Ls=Ls.todense()
Lt=Lt.todense()
LsLs=LsLs.todense()
LtLt=LtLt.todense()
#off we go
dt=datetime.now()-t1
print 'Preprocessing wall time was '+str(dt)
print '\n--- RUNNING INVERSIONS ---\n'
ttotal=datetime.now()
kout=0
for kt in range(len(reg_temporal)):
for ks in range(len(reg_spatial)):
t1=datetime.now()
lambda_spatial=reg_spatial[ks]
lambda_temporal=reg_temporal[kt]
print 'Running inversion '+str(kout+1)+' of '+str(Ninversion)+' at regularization levels: ls ='+repr(lambda_spatial)+' , lt = '+repr(lambda_temporal)
# DEG!! HERE THERE NEEDS TO BE SOME LOOP THROUGH THE LAMBDA_SPATIALS FOR EACH FAULT SEGMENT
Kinv=K+(lambda_spatial**2)*+(lambda_temporal**2)*LtLt
if solver.lower()=='lstsq':
sol,res,rank,s=lstsq(Kinv,x)
elif solver.lower()=='nnls':
x=squeeze(x.T)
sol,res=nnls(Kinv,x)
x=expand_dims(x,axis=1)
sol=expand_dims(sol,axis=1)
else:
print 'ERROR: Unrecognized solver \''+solver+'\''
#Compute synthetics
ds=dot(G,sol)
#Get stats
L2,Lmodel=inv.get_stats(Kinv,sol,x)
VR=inv.get_VR(G,sol,d)
ABIC=inv.get_ABIC(G,K,sol,d,lambda_spatial,lambda_temporal,Ls,LsLs,Lt,LtLt)
## CHANGE THIS FOR SEGS!!!! <-- DEG
#ABIC=inv.get_ABIC(G,K,sol,d,lambda_spatial,lambda_temporal,Ls,LsLs,Lt,LtLt,nfaults,segs)
#Get moment
Mo,Mw=inv.get_moment(home,project_name,fault_name,model_name,sol)
#If a rotational offset was applied then reverse it for output to file
if beta !=0:
sol=inv.rot2ds(sol,beta)
#Write log
inv.write_log(home,project_name,run_name,kout,rupture_speed,num_windows,lambda_spatial,lambda_temporal,beta,
L2,Lmodel,VR,ABIC,Mo,Mw,model_name,fault_name,G_name,GF_list,solver)
#Write output to file
inv.write_synthetics(home,project_name,run_name,GF_list,G,sol,ds,kout,decimate)
inv.write_model(home,project_name,run_name,fault_name,model_name,rupture_speed,num_windows,epicenter,sol,kout)
kout+=1
dt1=datetime.now()-t1
dt2=datetime.now()-ttotal
print '... inversion wall time was '+str(dt1)+', total wall time elapsed is '+str(dt2)
def run_inversion(home,project_name,run_name,fault_name,model_name,GF_list,G_from_file,G_name,epicenter,
rupture_speed,num_windows,reg_spatial,reg_temporal,nfaults,beta,decimate,bandpass,
solver,bounds,weight=False,Ltype=2,target_moment=None,data_vector=None):
'''
Assemble G and d, determine smoothing and run the inversion
'''
from mudpy import inverse as inv
from mudpy.forward import get_mu_and_area
from numpy import zeros,dot,array,squeeze,expand_dims,empty,tile,eye,ones,arange,load
from numpy.linalg import lstsq
from scipy.sparse import csr_matrix as sparse
from scipy.optimize import nnls
from datetime import datetime
import gc
t1=datetime.now()
#Get data vector
if data_vector==None:
d=inv.getdata(home,project_name,GF_list,decimate,bandpass=None)
else:
d=load(data_vector)
#Get GFs
G=inv.getG(home,project_name,fault_name,model_name,GF_list,G_from_file,G_name,epicenter,
rupture_speed,num_windows,decimate,bandpass)
gc.collect()
#Get data weights
if weight==True:
print 'Applying data weights'
w=inv.get_data_weights(home,project_name,GF_list,d,decimate)
W=empty(G.shape)
W=tile(w,(G.shape[1],1)).T
WG=empty(G.shape)
WG=W*G
wd=w*d.squeeze()
wd=expand_dims(wd,axis=1)
#Clear up extraneous variables
W=None
w=None
#Define inversion quantities
x=WG.transpose().dot(wd)
print 'Computing G\'G'
K=(WG.T).dot(WG)
else:
#Define inversion quantities if no weightd
x=G.transpose().dot(d)
print 'Computing G\'G'
K=(G.T).dot(G)
#Get regularization matrices (set to 0 matrix if not needed)
static=False #Is it jsut a static inversion?
if reg_spatial!=None:
if Ltype==2: #Laplacian smoothing
Ls=inv.getLs(home,project_name,fault_name,nfaults,num_windows,bounds)
elif Ltype==0: #Tikhonov smoothing
N=nfaults[0]*nfaults[1]*num_windows*2 #Get total no. of model parameters
Ls=eye(N)
elif Ltype==3: #moment regularization
N=nfaults[0]*nfaults[1]*num_windows*2 #Get total no. of model parameters
Ls=ones((1,N))
#Add rigidity and subfault area
mu,area=get_mu_and_area(home,project_name,fault_name,model_name)
istrike=arange(0,N,2)
Ls[0,istrike]=area*mu
idip=arange(1,N,2)
Ls[0,idip]=area*mu
#Modify inversion quantities
x=x+Ls.T.dot(target_moment)
else:
print 'ERROR: Unrecognized regularization type requested'
return
Ninversion=len(reg_spatial)
else:
Ls=zeros(K.shape)
reg_spatial=array([0.])
Ninversion=1
if reg_temporal!=None:
Lt=inv.getLt(home,project_name,fault_name,num_windows)
Ninversion=len(reg_temporal)*Ninversion
else:
Lt=zeros(K.shape)
reg_temporal=array([0.])
static=True
#Make L's sparse
Ls=sparse(Ls)
Lt=sparse(Lt)
#Get regularization tranposes for ABIC
LsLs=Ls.transpose().dot(Ls)
LtLt=Lt.transpose().dot(Lt)
#inflate
Ls=Ls.todense()
Lt=Lt.todense()
LsLs=LsLs.todense()
LtLt=LtLt.todense()
#off we go
dt=datetime.now()-t1
print 'Preprocessing wall time was '+str(dt)
print '\n--- RUNNING INVERSIONS ---\n'
ttotal=datetime.now()
kout=0
for kt in range(len(reg_temporal)):
for ks in range(len(reg_spatial)):
t1=datetime.now()
lambda_spatial=reg_spatial[ks]
lambda_temporal=reg_temporal[kt]
print 'Running inversion '+str(kout+1)+' of '+str(Ninversion)+' at regularization levels: ls ='+repr(lambda_spatial)+' , lt = '+repr(lambda_temporal)
if static==True: #Only statics inversion no Lt matrix
Kinv=K+(lambda_spatial**2)*LsLs
Lt=eye(len(K))
LtLt=Lt.T.dot(Lt)
else: #Mixed inversion
Kinv=K+(lambda_spatial**2)*LsLs+(lambda_temporal**2)*LtLt
if solver.lower()=='lstsq':
sol,res,rank,s=lstsq(Kinv,x)
elif solver.lower()=='nnls':
x=squeeze(x.T)
try:
sol,res=nnls(Kinv,x)
except:
print '+++ WARNING: No solution found, writting zeros.'
sol=zeros(G.shape[1])
x=expand_dims(x,axis=1)
sol=expand_dims(sol,axis=1)
else:
print 'ERROR: Unrecognized solver \''+solver+'\''
#Compute synthetics
ds=dot(G,sol)
#Get stats
L2,Lmodel=inv.get_stats(Kinv,sol,x)
VR=inv.get_VR(home,project_name,GF_list,sol,d,ds,decimate)
#VR=inv.get_VR(WG,sol,wd)
#ABIC=inv.get_ABIC(WG,K,sol,wd,lambda_spatial,lambda_temporal,Ls,LsLs,Lt,LtLt)
ABIC=inv.get_ABIC(G,K,sol,d,lambda_spatial,lambda_temporal,Ls,LsLs,Lt,LtLt)
#Get moment
Mo,Mw=inv.get_moment(home,project_name,fault_name,model_name,sol)
#If a rotational offset was applied then reverse it for output to file
if beta !=0:
sol=inv.rot2ds(sol,beta)
#Write log
inv.write_log(home,project_name,run_name,kout,rupture_speed,num_windows,lambda_spatial,lambda_temporal,beta,
L2,Lmodel,VR,ABIC,Mo,Mw,model_name,fault_name,G_name,GF_list,solver)
#Write output to file
inv.write_synthetics(home,project_name,run_name,GF_list,G,sol,ds,kout,decimate)
inv.write_model(home,project_name,run_name,fault_name,model_name,rupture_speed,num_windows,epicenter,sol,kout)
kout+=1
dt1=datetime.now()-t1
dt2=datetime.now()-ttotal
print '... inversion wall time was '+str(dt1)+', total wall time elapsed is '+str(dt2)
